Reasons for Enthusiasm and Caution Regarding Big Data in Applied Selection Research

Big Data and Big Data Analytics (BD and BDA, respectively) have burst onto the scene in the popular culture in recent years, perhaps most notably in the form of Nate Silver’s high profile predictions in the political and professional sports arenas. According to futurist Tyler Cowen, modern society will soon be reordered, with creators and tenders of incredibly complex datasets and the artificial intelligences mining those datasets at the top and the remaining humans relegated to a vast underclass (Cowen, 2013). SIOP’s interest in BD and BDA’s potential talent management applications has increased as well. Case in point, this article is the third on the topic to be published in TIP in the last five issues. Whereas the previous articles (Maurath, 2014; Poeppelman, Blacksmith, & Yang, 2013) viewed BD and BDA through a wide-angle lens, we focus more narrowly on their applied selection research applications. Although we see the potential for BD and BDA to provide considerable incremental value, the current article can be considered a cautionary editorial on their merits in this context. We focus particularly on assessment design and validation, highlighting areas where BD and BDA can contribute (and are already doing so) while also identifying areas where they are unlikely to bear fruit, may pose risks, or are potentially unnecessary. The article is organized in terms of recent definitions of Big Data (Laney, 2001; Maurath, 2014). Specifically, Big Data is defined in terms of “three Vs” —volume, velocity, and variety—and we discuss each of these aspects in turn. In preparing for this article, we spoke with A. James Illingworth and Michael Lippstreu, two of the authors of the selection and assessment chapter in the forthcoming SIOP Organizational Frontiers Series book Big Data at work: The Data Science Revolution and Organizational Psychology (Illingworth, Lippstreu, & Deprez-Sims, 2015). At appropriate points throughout, we share their perspectives on Big Data’s role in applied selection research and talent acquisition/management.